Developing a Structural Equation Modeling of the Role of Academic Stressors on Resilience, Motivation and Academic Burnout Among Pre-University Female in Isfahan

The purpose of this study was to develop a structural equation model of the role of academic stressors on resiliency, motivation and academic burnout among female participants of entrance exam of University in Isfahan. The population was female participants of entrance exam. Two hundred pre-university females were selected by random multistage method as samples of this   study. The questionnaires: The Classroom and School Community (Rovai, 2002), Sarason Test Anxiety (1980), Frost Multidimensional Perfectionism (1990), Ego-Resiliency (Klohnen, 1996), Academic Motivation (Vallerand, 1992), Academic Burnout (Breso, 2007) were used. Confirmatory Factor Analysis (CFA) was used for studying structural equation and AMOS-1 was used for evaluating suggested model. According to proper fitting of final model, this model was verified and accepted. The results of research showed that the academic stressors were effective on academic burnout variables, academic motivation and resiliency variable. Also resiliency, as a mediator variable of academic stressors, led to educational inefficiency. The increase of academic stressors decreased academic motivation, especially internal motivation and resiliency, as a result the educational inefficiency increased. These pressures predicted academic burnout

Analgesic effects of paracetamol and morphine after elective laparotomy surgeries

Background: Opioids have been traditionally used for postoperative pain control, but they have some unpleasant side effects such as respiratory depression or nausea. Some other analgesic drugs like non-steroidal anti-inflammatory drugs (NSAIDs) are also being used for pain management due to their fewer side effects. Objectives: The aim of our study was to compare the analgesic effects of paracetamol, an intravenous non-opioid analgesic and morphine infusion after elective laparotomy surgeries. Patients and Methods: This randomized clinical study was performed on 157 ASA (American Society of Anesthesiology) I-II patients, who were scheduled for elective laparotomy. These patients were managed by general anesthesia with TIVA technique in both groups and 150 patients were analyzed. Paracetamol (4 g/24 hours) in group 1 and morphine (20 mg/24 hours) in group 2 were administered by infusion pump after surgery. Postoperative pain evaluation was performed by visual analog scale (VAS) during several hours postoperatively. Meperidine was administered for patients complaining of pain with VAS > 3 and repeated if essential. Total doses of infused analgesics, were recorded following the surgery and compared. Analysis was performed on the basis of VAS findings and meperidine consumption. Results: There were no differences in demographic data between two groups. Significant difference in pain score was found between the two groups, in the first eight hours following operation (P value = 0.00), but not after 12 hours (P = 0.14).The total dose of rescue drug (meperidine) and number of doses injected showed a meaningful difference between the two groups (P = 0.00). Also nausea, vomiting and itching showed a significant difference between the two groups and patients in morphine group, experienced higher levels of them. Conclusions: Paracetamol is not enough for postoperative pain relief in the first eight hour postoperatively, but it can reduce postoperative opioid need and is efficient enough for pain management as morphine after the first eight hours following surgery. © 2014, Iranian Society of Regional Anesthesia and Pain Medicine (ISRAPM)

Anxiety and depression among infertile women: a cross-sectional survey from Hungary

BACKGROUND: Infertility is often associated with a chronic state of stress which may manifest itself in anxiety-related and depressive symptoms. The aim of our study is to assess the psychological state of women with and without fertility problems, and to investigate the background factors of anxiety-related and depressive symptoms in women struggling with infertility. METHODS: Our study was conducted with the participation of 225 (134 primary infertile and 91 fertile) women, recruited in a clinical setting and online. We used the following questionnaires: Spielberger Trait Anxiety Inventory (STAI-T), Shortened Beck Depression Inventory (BDI) and Fertility Problem Inventory (FPI). We also interviewed our subjects on the presence of other sources of stress (the quality of the relationship with their mother, financial and illness-related stress), and we described sociodemographic and fertility-specific characteristics. We tested our hypotheses using independent-samples t-tests (M +/- SD) and multiple linear regression modelling (ss). RESULTS: Infertile women were younger (33.30 +/- 4.85 vs. 35.74 +/- 5.73, p = .001), but had significantly worse psychological well-being (BDI = 14.94 +/- 12.90 vs. 8.95 +/- 10.49, p < .0001; STAI-T = 48.76 +/- 10.96 vs. 41.18 +/- 11.26, p < .0001) than fertile subjects. Depressive symptoms and anxiety in infertile women were associated with age, social concern, sexual concern and maternal relationship stress. Trait anxiety was also associated with financial stress. Our model was able to account for 58% of the variance of depressive symptoms and 62% of the variance of trait anxiety. CONCLUSIONS: Depressive and anxiety-related symptoms of infertile women are more prominent than those of fertile females. The measurement of these indicators and the mitigation of underlying distress by adequate psychosocial interventions should be encouraged

Proactive Middleware for Fault Detection and Advanced Conflict Handling in Sensor Fusion

Robots traditionally have a wide array of sensors that allow them to react to the environment and make appropriate decisions. These sensors can give incorrect or imprecise data due to malfunctioning or noise. Sensor fusion methods try to overcome some of these issues by using the data coming from different sensors and combining it. However, they often don’t take sensor malfunctioning and a priori knowledge about the sensors and the environment into account, which can produce conflicting information for the robot to work with. In this paper, we present an architecture and process in order to overcome some of these limitations based on a proactive rule-based system